David J. Field
Cognitive Neuroscience · 1987–2005
A researcher in the Science corpus: 8 papers; trusts 20 researchers and is trusted by 4,310 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 25 citations into it; #1,238 of 10,852 by trust here, #1,731 by citations
- Neuroscience — 19 citations into it; #7 of 487 by trust here, #13 by citations
- Computer Vision and Pattern Recognition — 12 citations into it; #507 of 21,575 by trust here, #1,408 by citations
- Representation & Self-Supervised Learning — 12 citations into it; #100 of 3,145 by trust here, #188 by citations
- Cellular and Molecular Neuroscience — 9 citations into it; #324 of 2,998 by trust here, #802 by citations
- Signal Processing — 9 citations into it
- Artificial Intelligence — 6 citations into it
- Electrical and Electronic Engineering — 5 citations into it
Whom David J. Field trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ralph Linsker
- Terence D. Sanger
- John G. Daugman
- Stéphane Mallat
- Barbara Olshausen
- D. Kersten
- Richard S. Zemel
- J. Urgen Schmidhuber
- Anthony J. Bell
- David H. Hubel
Who trusts David J. Field overall
- Yoshua Bengio
- Antonio Torralba
- Aapo Hyvärinen
- Song-Chun Zhu
- Quoc V. Le
- Abhinav Kumar Gupta
- Luc Van Gool
- Dacheng Tao
- Julien Mairal
- Kyunghyun Cho
Papers from David J. Field's perspective
Ordered by how strongly the researchers David J. Field trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Emergence of simple-cell receptive field properties by learning a sparse code for natural images (1996)
- How Close Are We to Understanding V1? (2005) — 478 citations
- Sparse coding of sensory inputs. (2004) — 1,546 citations
- Sparse coding with an overcomplete basis set: a strategy employed by V1? (1997)
- Natural image statistics and efficient coding. (1996)
- What Is the Goal of Sensory Coding? (1994)
- What The Statistics Of Natural Images Tell Us About Visual Coding (1989)
- Relations between the statistics of natural images and the response properties of cortical cells. (1987)